Dual-dictionary learning based MR image reconstruction with self-adaptive dictionaries

Dual-dictionary learning method utilizes two dictionaries at two different resolution levels, a high resolution dictionary trained with full-data training set, and a low resolution dictionary co-trained with corresponding undersampled dataset. This method effectively incorporates a priori knowledge of typical structures, specific features and local details, leading to its success in magnetic resonance (MR) image reconstruction from highly undersampled k-space data. In this paper, we improve this dual-dictionary learning method by using self-adaptive dictionaries. The two level dictionaries are updated correspondingly in the inner iteration after updating the reconstruction result to maintain their matching accuracy. Experimental results show that the proposed method can improve the reconstruction quality efficiently and enhance the robustness significantly.